🧪 Mauzalyzer

Ultimate Data Duplication & Similarity Analyzer — built for developers, analysts, and data engineers who need quick insights from messy, large, or structured data files.

Created by mauzware
Works on Linux 🐧 and Windows 🧩
Fast, powerful, and customizable via CLI ⚙️

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📦 Features

• 🔁 Detects exact duplicates across rows and columns
• 🔍 Finds similar values using fuzzy matching
• 🧠 Automatically categorizes values: Numeric, Textual, Mixed, Unknown
• 📊 Outputs detailed summaries and top frequent values
• 💾 Exports reports in JSON, TXT, or XML
• 📁 Supports both CSV and XLSX files
• ⚡ Modes: Fast, Standard, and Detailed
• 🧼 Experimental support for messy CSVs (--messy)
• 🌈 Colorful CLI with optional logging and quiet/debug modes

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🛠️ Installation

Make sure you have Python 3.9+ installed (tested on 3.13+).
You can use either pip or pip3, whichever works on your system depending on your Python version.

Windows

git clone https://github.com/mauzware/Mauzalyzer.git
cd Mauzalyzer
pip install -r requirements.txt
python mauzalyzer.py --help

Linux Debian/Ubuntu

git clone https://github.com/mauzware/Mauzalyzer.git
cd Mauzalyzer
pip install -r requirements.txt
python mauzalyzer.py --help

Kali Linux

In Kali, all required modules are already pre-installed.

git clone https://github.com/mauzware/Mauzalyzer.git
cd Mauzalyzer
python mauzalyzer.py --help

If you are missing some modules by any chance, you can install them with:

1. Create a virtual environment and use: pip3 install -r requirements.txt
   
2. Install them manually with apt: sudo apt install python3-[module_name]

Virtual Environment Setup

sudo apt update
sudo apt install python3-venv -y

git clone https://github.com/mauzware/Mauzalyzer.git
cd Mauzalyzer
python3 -m venv Mauzalyzer-env
source Mauzalyzer-env/bin/activate
pip install -r requirements.txt
deactivate

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🖥️ Usage

You can use either python or python3, whichever works on your system depending on your Python version.

python mauzalyzer.py [OPTIONS] source
python3 mauzalyzer.py [OPTIONS] source

Examples:

python3 mauzalyzer.py Your_File.csv #Basic scan
python mauzalyzer.py Your_File.xlsx #Basic scan
python3 mauzalyzer.py Your_File.csv --detailed #Detailed scan
python mauzalyzer.py --fast Your_File.xlsx #Fast scan
python3 mauzalyzer.py Your_File.csv -o Report_Name --output-format=txt #Saving output in TXT format
python mauzalyzer.py Your_File.xlsx -o Report_Name --output-format=xml #Saving output in XML format

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🔧 Basic Options

Option	Description
--fast	Fast scanning (basic checks only)
--detailed	Detailed scanning with deep similarity analysis
--type csv/xlsx	Manually set the file type
--chunksize	Set custom chunk size for large files
--messy	Preprocess messy CSV files
-o, --output	Custom output file name
--output-format	Output format: json, txt, xml

🛡️ Utility Flags

Flag	Description
--version	Display version and author info
--help	Show help screen
--debug	Enable full debug traceback
--quiet	Suppress all output
-v, --verbose	Show verbose output

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📸 Screenshots

💡 Help menu on Linux:

💡 Help menu on Windows:

💡 Mauzalyzer in action:

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📂 Output Example

{
  "analysis_date": "2025-04-17T17:03:33",
  "data_source": "Your_Input_File.xlsx",
  "findings": [...],
  "summary": {...}
}

Reports are saved to the data_report/ folder and include a timestamp + hash for uniqueness.
data_report/ folder will be automatically created after first usage.

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⚡ Bonus: Optional Header Row Removal, details are below.

This code helps remove repeated or stray header rows inside messy CSVs (usually when a report was exported from Excel or multiple tables were merged).

❗️ When to use:

• You scanned a file and noticed weird duplicated values like "type" or "sale_date"
• You know your file includes repeated headers (you may have seen them in Excel file when you opened it)

📋 Instructions:

1. Below this comment, you'll see a method called 'remove_headers(df)', edit the list 'known_headers' to include any words you want to treat as "header rows".
2. In regards to editing 'known_headers', you can add more values or remove some, it's completely on you.
3. Go to method 'scan_csv()' in the code, you'll see '#df = remove_headers(df)', just remove # and that's it, voila removed headers are implemented.

Example:

def scan_csv(self, similarity_threshold=85):
    try:
        df = self.safe_read_csv(file_path)
        #df = remove_headers(df) <-- Here, simply delete # and its done

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🚧 Future Plans: Mauzalyzer v2.0 (coming soon...)

Mauzalyzer Engineers are already cooking up new features for v2.0. Stay tuned! 👾

• 👁️ Better schema detection for extremely messy files

• 🗂️ Support for more formats: JSON, XML, TXT (as inputs)

• 🎛️ GUI mode (TBD)

• 🔧 Interactive mode for manual value inspection

• ⚙️ Additional CLI support

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👨‍💻 Author

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📜 License

This project is open-source and distributed under the terms of the MIT License. You are free to use, modify, and distribute it with proper attribution.

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All kuddos go to my professor who taught me everything I know, I think she will be proud of me using this many emojis. 😅
To all my friends who supported me on this wonderful journey — I haven't forgotten you, folks. Big thanks and much love to all of you! ❤️